/*!
 * Color Thief v2.0
 * by Lokesh Dhakar - http://www.lokeshdhakar.com
 *
 * Thanks
 * ------
 * Nick Rabinowitz - For creating quantize.js.
 * John Schulz - For clean up and optimization. @JFSIII
 * Nathan Spady - For adding drag and drop support to the demo page.
 *
 * License
 * -------
 * Copyright 2011, 2015 Lokesh Dhakar
 * Released under the MIT license
 * https://raw.githubusercontent.com/lokesh/color-thief/master/LICENSE
 *
 */

/*
  CanvasImage Class
  Class that wraps the html image element and canvas.
  It also simplifies some of the canvas context manipulation
  with a set of helper functions.
*/
var CanvasImage = function (image) {
  this.canvas = document.createElement('canvas')
  this.context = this.canvas.getContext('2d')

  document.body.appendChild(this.canvas)

  this.width = this.canvas.width = image.width
  this.height = this.canvas.height = image.height

  this.context.drawImage(image, 0, 0, this.width, this.height)
}

CanvasImage.prototype.clear = function () {
  this.context.clearRect(0, 0, this.width, this.height)
}

CanvasImage.prototype.update = function (imageData) {
  this.context.putImageData(imageData, 0, 0)
}

CanvasImage.prototype.getPixelCount = function () {
  return this.width * this.height
}

CanvasImage.prototype.getImageData = function () {
  return this.context.getImageData(0, 0, this.width, this.height)
}

CanvasImage.prototype.removeCanvas = function () {
  this.canvas.parentNode.removeChild(this.canvas)
}

var ColorThief = function () {}

/*
 * getColor(sourceImage[, quality])
 * returns {r: num, g: num, b: num}
 *
 * Use the median cut algorithm provided by quantize.js to cluster similar
 * colors and return the base color from the largest cluster.
 *
 * Quality is an optional argument. It needs to be an integer. 1 is the highest quality settings.
 * 10 is the default. There is a trade-off between quality and speed. The bigger the number, the
 * faster a color will be returned but the greater the likelihood that it will not be the visually
 * most dominant color.
 *
 * */
ColorThief.prototype.getColor = function (sourceImage, quality) {
  var palette = this.getPalette(sourceImage, 5, quality)
  var dominantColor = palette[0]
  return dominantColor
}

ColorThief.prototype.getPalette = function (sourceImage, quality) {
  return this.getPalette(sourceImage, 5, quality)
}

/*
 * getPalette(sourceImage[, colorCount, quality])
 * returns array[ {r: num, g: num, b: num}, {r: num, g: num, b: num}, ...]
 *
 * Use the median cut algorithm provided by quantize.js to cluster similar colors.
 *
 * colorCount determines the size of the palette; the number of colors returned. If not set, it
 * defaults to 10.
 *
 * BUGGY: Function does not always return the requested amount of colors. It can be +/- 2.
 *
 * quality is an optional argument. It needs to be an integer. 1 is the highest quality settings.
 * 10 is the default. There is a trade-off between quality and speed. The bigger the number, the
 * faster the palette generation but the greater the likelihood that colors will be missed.
 *
 *
 */
ColorThief.prototype.getPalette = function (sourceImage, colorCount, quality) {
  if (typeof colorCount === 'undefined') {
    colorCount = 10
  }
  if (typeof quality === 'undefined' || quality < 1) {
    quality = 10
  }

  // Create custom CanvasImage object
  var image = new CanvasImage(sourceImage)
  var imageData = image.getImageData()
  var pixels = imageData.data
  var pixelCount = image.getPixelCount()

  // Store the RGB values in an array format suitable for quantize function
  var pixelArray = []
  for (var i = 0, offset, r, g, b, a; i < pixelCount; i = i + quality) {
    offset = i * 4
    r = pixels[offset + 0]
    g = pixels[offset + 1]
    b = pixels[offset + 2]
    a = pixels[offset + 3]
    // If pixel is mostly opaque and not white
    if (a >= 125) {
      if (!(r > 250 && g > 250 && b > 250)) {
        pixelArray.push([r, g, b])
      }
    }
  }

  // Send array to quantize function which clusters values
  // using median cut algorithm
  var cmap = MMCQ.quantize(pixelArray, colorCount)
  var palette = cmap ? cmap.palette() : null

  // Clean up
  image.removeCanvas()

  return palette
}

/*!
 * quantize.js Copyright 2008 Nick Rabinowitz.
 * Licensed under the MIT license: http://www.opensource.org/licenses/mit-license.php
 */

// fill out a couple protovis dependencies
/*!
 * Block below copied from Protovis: http://mbostock.github.com/protovis/
 * Copyright 2010 Stanford Visualization Group
 * Licensed under the BSD License: http://www.opensource.org/licenses/bsd-license.php
 */
if (!pv) {
  var pv = {
    map: function (array, f) {
      var o = {}
      return f ? array.map(function (d, i) { o.index = i; return f.call(o, d) }) : array.slice()
    },
    naturalOrder: function (a, b) {
      return (a < b) ? -1 : ((a > b) ? 1 : 0)
    },
    sum: function (array, f) {
      var o = {}
      return array.reduce(f ? function (p, d, i) { o.index = i; return p + f.call(o, d) } : function (p, d) { return p + d }, 0)
    },
    max: function (array, f) {
      return Math.max.apply(null, f ? pv.map(array, f) : array)
    }
  }
}

/**
 * Basic Javascript port of the MMCQ (modified median cut quantization)
 * algorithm from the Leptonica library (http://www.leptonica.com/).
 * Returns a color map you can use to map original pixels to the reduced
 * palette. Still a work in progress.
 *
 * @author Nick Rabinowitz
 * @example

// array of pixels as [R,G,B] arrays
var myPixels = [[190,197,190], [202,204,200], [207,214,210], [211,214,211], [205,207,207]
                // etc
                ];
var maxColors = 4;

var cmap = MMCQ.quantize(myPixels, maxColors);
var newPalette = cmap.palette();
var newPixels = myPixels.map(function(p) {
    return cmap.map(p);
});

 */
var MMCQ = (function () {
  // private constants
  var sigbits = 5,
    rshift = 8 - sigbits,
    maxIterations = 1000,
    fractByPopulations = 0.75

    // get reduced-space color index for a pixel
  function getColorIndex (r, g, b) {
    return (r << (2 * sigbits)) + (g << sigbits) + b
  }

  // Simple priority queue
  function PQueue (comparator) {
    var contents = [],
      sorted = false

    function sort () {
      contents.sort(comparator)
      sorted = true
    }

    return {
      push: function (o) {
        contents.push(o)
        sorted = false
      },
      peek: function (index) {
        if (!sorted) sort()
        if (index === undefined) index = contents.length - 1
        return contents[index]
      },
      pop: function () {
        if (!sorted) sort()
        return contents.pop()
      },
      size: function () {
        return contents.length
      },
      map: function (f) {
        return contents.map(f)
      },
      debug: function () {
        if (!sorted) sort()
        return contents
      }
    }
  }

  // 3d color space box
  function VBox (r1, r2, g1, g2, b1, b2, histo) {
    var vbox = this
    vbox.r1 = r1
    vbox.r2 = r2
    vbox.g1 = g1
    vbox.g2 = g2
    vbox.b1 = b1
    vbox.b2 = b2
    vbox.histo = histo
  }
  VBox.prototype = {
    volume: function (force) {
      var vbox = this
      if (!vbox._volume || force) {
        vbox._volume = ((vbox.r2 - vbox.r1 + 1) * (vbox.g2 - vbox.g1 + 1) * (vbox.b2 - vbox.b1 + 1))
      }
      return vbox._volume
    },
    count: function (force) {
      var vbox = this,
        histo = vbox.histo,
        index = 0
      if (!vbox._count_set || force) {
        var npix = 0,
          i, j, k
        for (i = vbox.r1; i <= vbox.r2; i++) {
          for (j = vbox.g1; j <= vbox.g2; j++) {
            for (k = vbox.b1; k <= vbox.b2; k++) {
              index = getColorIndex(i, j, k)
              npix += (histo[index] || 0)
            }
          }
        }
        vbox._count = npix
        vbox._count_set = true
      }
      return vbox._count
    },
    copy: function () {
      var vbox = this
      return new VBox(vbox.r1, vbox.r2, vbox.g1, vbox.g2, vbox.b1, vbox.b2, vbox.histo)
    },
    avg: function (force) {
      var vbox = this,
        histo = vbox.histo
      if (!vbox._avg || force) {
        var ntot = 0,
          mult = 1 << (8 - sigbits),
          rsum = 0,
          gsum = 0,
          bsum = 0,
          hval,
          i, j, k, histoindex
        for (i = vbox.r1; i <= vbox.r2; i++) {
          for (j = vbox.g1; j <= vbox.g2; j++) {
            for (k = vbox.b1; k <= vbox.b2; k++) {
              histoindex = getColorIndex(i, j, k)
              hval = histo[histoindex] || 0
              ntot += hval
              rsum += (hval * (i + 0.5) * mult)
              gsum += (hval * (j + 0.5) * mult)
              bsum += (hval * (k + 0.5) * mult)
            }
          }
        }
        if (ntot) {
          vbox._avg = [~~(rsum / ntot), ~~(gsum / ntot), ~~(bsum / ntot)]
        } else {
          //                    console.log('empty box');
          vbox._avg = [
            ~~(mult * (vbox.r1 + vbox.r2 + 1) / 2),
            ~~(mult * (vbox.g1 + vbox.g2 + 1) / 2),
            ~~(mult * (vbox.b1 + vbox.b2 + 1) / 2)
          ]
        }
      }
      return vbox._avg
    },
    contains: function (pixel) {
      var vbox = this,
        rval = pixel[0] >> rshift
      gval = pixel[1] >> rshift
      bval = pixel[2] >> rshift
      return (rval >= vbox.r1 && rval <= vbox.r2 &&
                    gval >= vbox.g1 && gval <= vbox.g2 &&
                    bval >= vbox.b1 && bval <= vbox.b2)
    }
  }

  // Color map
  function CMap () {
    this.vboxes = new PQueue(function (a, b) {
      return pv.naturalOrder(
        a.vbox.count() * a.vbox.volume(),
        b.vbox.count() * b.vbox.volume()
      )
    })
  }
  CMap.prototype = {
    push: function (vbox) {
      this.vboxes.push({
        vbox: vbox,
        color: vbox.avg()
      })
    },
    palette: function () {
      return this.vboxes.map(function (vb) { return vb.color })
    },
    size: function () {
      return this.vboxes.size()
    },
    map: function (color) {
      var vboxes = this.vboxes
      for (var i = 0; i < vboxes.size(); i++) {
        if (vboxes.peek(i).vbox.contains(color)) {
          return vboxes.peek(i).color
        }
      }
      return this.nearest(color)
    },
    nearest: function (color) {
      var vboxes = this.vboxes,
        d1, d2, pColor
      for (var i = 0; i < vboxes.size(); i++) {
        d2 = Math.sqrt(
          Math.pow(color[0] - vboxes.peek(i).color[0], 2) +
                    Math.pow(color[1] - vboxes.peek(i).color[1], 2) +
                    Math.pow(color[2] - vboxes.peek(i).color[2], 2)
        )
        if (d2 < d1 || d1 === undefined) {
          d1 = d2
          pColor = vboxes.peek(i).color
        }
      }
      return pColor
    },
    forcebw: function () {
      // XXX: won't  work yet
      var vboxes = this.vboxes
      vboxes.sort(function (a, b) { return pv.naturalOrder(pv.sum(a.color), pv.sum(b.color)) })

      // force darkest color to black if everything < 5
      var lowest = vboxes[0].color
      if (lowest[0] < 5 && lowest[1] < 5 && lowest[2] < 5) { vboxes[0].color = [0, 0, 0] }

      // force lightest color to white if everything > 251
      var idx = vboxes.length - 1,
        highest = vboxes[idx].color
      if (highest[0] > 251 && highest[1] > 251 && highest[2] > 251) { vboxes[idx].color = [255, 255, 255] }
    }
  }

  // histo (1-d array, giving the number of pixels in
  // each quantized region of color space), or null on error
  function getHisto (pixels) {
    var histosize = 1 << (3 * sigbits),
      histo = new Array(histosize),
      index, rval, gval, bval
    pixels.forEach(function (pixel) {
      rval = pixel[0] >> rshift
      gval = pixel[1] >> rshift
      bval = pixel[2] >> rshift
      index = getColorIndex(rval, gval, bval)
      histo[index] = (histo[index] || 0) + 1
    })
    return histo
  }

  function vboxFromPixels (pixels, histo) {
    var rmin = 1000000, rmax = 0,
      gmin = 1000000, gmax = 0,
      bmin = 1000000, bmax = 0,
      rval, gval, bval
    // find min/max
    pixels.forEach(function (pixel) {
      rval = pixel[0] >> rshift
      gval = pixel[1] >> rshift
      bval = pixel[2] >> rshift
      if (rval < rmin) rmin = rval
      else if (rval > rmax) rmax = rval
      if (gval < gmin) gmin = gval
      else if (gval > gmax) gmax = gval
      if (bval < bmin) bmin = bval
      else if (bval > bmax) bmax = bval
    })
    return new VBox(rmin, rmax, gmin, gmax, bmin, bmax, histo)
  }

  function medianCutApply (histo, vbox) {
    if (!vbox.count()) return

    var rw = vbox.r2 - vbox.r1 + 1,
      gw = vbox.g2 - vbox.g1 + 1,
      bw = vbox.b2 - vbox.b1 + 1,
      maxw = pv.max([rw, gw, bw])
    // only one pixel, no split
    if (vbox.count() == 1) {
      return [vbox.copy()]
    }
    /* Find the partial sum arrays along the selected axis. */
    var total = 0,
      partialsum = [],
      lookaheadsum = [],
      i, j, k, sum, index
    if (maxw == rw) {
      for (i = vbox.r1; i <= vbox.r2; i++) {
        sum = 0
        for (j = vbox.g1; j <= vbox.g2; j++) {
          for (k = vbox.b1; k <= vbox.b2; k++) {
            index = getColorIndex(i, j, k)
            sum += (histo[index] || 0)
          }
        }
        total += sum
        partialsum[i] = total
      }
    } else if (maxw == gw) {
      for (i = vbox.g1; i <= vbox.g2; i++) {
        sum = 0
        for (j = vbox.r1; j <= vbox.r2; j++) {
          for (k = vbox.b1; k <= vbox.b2; k++) {
            index = getColorIndex(j, i, k)
            sum += (histo[index] || 0)
          }
        }
        total += sum
        partialsum[i] = total
      }
    } else { /* maxw == bw */
      for (i = vbox.b1; i <= vbox.b2; i++) {
        sum = 0
        for (j = vbox.r1; j <= vbox.r2; j++) {
          for (k = vbox.g1; k <= vbox.g2; k++) {
            index = getColorIndex(j, k, i)
            sum += (histo[index] || 0)
          }
        }
        total += sum
        partialsum[i] = total
      }
    }
    partialsum.forEach(function (d, i) {
      lookaheadsum[i] = total - d
    })
    function doCut (color) {
      var dim1 = color + '1',
        dim2 = color + '2',
        left, right, vbox1, vbox2, d2, count2 = 0
      for (i = vbox[dim1]; i <= vbox[dim2]; i++) {
        if (partialsum[i] > total / 2) {
          vbox1 = vbox.copy()
          vbox2 = vbox.copy()
          left = i - vbox[dim1]
          right = vbox[dim2] - i
          if (left <= right) { d2 = Math.min(vbox[dim2] - 1, ~~(i + right / 2)) } else d2 = Math.max(vbox[dim1], ~~(i - 1 - left / 2))
          // avoid 0-count boxes
          while (!partialsum[d2]) d2++
          count2 = lookaheadsum[d2]
          while (!count2 && partialsum[d2 - 1]) count2 = lookaheadsum[--d2]
          // set dimensions
          vbox1[dim2] = d2
          vbox2[dim1] = vbox1[dim2] + 1
          //                    console.log('vbox counts:', vbox.count(), vbox1.count(), vbox2.count());
          return [vbox1, vbox2]
        }
      }
    }
    // determine the cut planes
    return maxw == rw ? doCut('r')
      : maxw == gw ? doCut('g')
        : doCut('b')
  }

  function quantize (pixels, maxcolors) {
    // short-circuit
    if (!pixels.length || maxcolors < 2 || maxcolors > 256) {
      //            console.log('wrong number of maxcolors');
      return false
    }

    // XXX: check color content and convert to grayscale if insufficient

    var histo = getHisto(pixels),
      histosize = 1 << (3 * sigbits)

    // check that we aren't below maxcolors already
    var nColors = 0
    histo.forEach(function () { nColors++ })
    if (nColors <= maxcolors) {
      // XXX: generate the new colors from the histo and return
    }

    // get the beginning vbox from the colors
    var vbox = vboxFromPixels(pixels, histo),
      pq = new PQueue(function (a, b) { return pv.naturalOrder(a.count(), b.count()) })
    pq.push(vbox)

    // inner function to do the iteration
    function iter (lh, target) {
      var ncolors = 1,
        niters = 0,
        vbox
      while (niters < maxIterations) {
        vbox = lh.pop()
        if (!vbox.count()) { /* just put it back */
          lh.push(vbox)
          niters++
          continue
        }
        // do the cut
        var vboxes = medianCutApply(histo, vbox),
          vbox1 = vboxes[0],
          vbox2 = vboxes[1]

        if (!vbox1) {
          //                    console.log("vbox1 not defined; shouldn't happen!");
          return
        }
        lh.push(vbox1)
        if (vbox2) { /* vbox2 can be null */
          lh.push(vbox2)
          ncolors++
        }
        if (ncolors >= target) return
        if (niters++ > maxIterations) {
          //                    console.log("infinite loop; perhaps too few pixels!");
          return
        }
      }
    }

    // first set of colors, sorted by population
    iter(pq, fractByPopulations * maxcolors)

    // Re-sort by the product of pixel occupancy times the size in color space.
    var pq2 = new PQueue(function (a, b) {
      return pv.naturalOrder(a.count() * a.volume(), b.count() * b.volume())
    })
    while (pq.size()) {
      pq2.push(pq.pop())
    }

    // next set - generate the median cuts using the (npix * vol) sorting.
    iter(pq2, maxcolors - pq2.size())

    // calculate the actual colors
    var cmap = new CMap()
    while (pq2.size()) {
      cmap.push(pq2.pop())
    }

    return cmap
  }

  return {
    quantize: quantize
  }
})()

export default ColorThief
